Pith. sign in

REVIEW 2 major objections 5 minor 60 references

An Exploratory Study on AI-driven Visualisation Techniques on Decision Making in Extended Reality

T0 review · 2 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read The paper argues that user preference among four AI-driven visualization techniques in XR is context-dependent, so no single style fits all decision tasks, and that preserving user autonomy and AI transparency are key design requirements.

desk verdict A modest, clearly written exploratory mapping of AI autonomy levels onto XR visualization; the context-dependence claim is plausible but the quantitative support is thinner than the prose suggests. read the letter →

arxiv 2507.10981 v1 pith:CNEZWALC submitted 2025-07-15 cs.HC

Ze Dong , Binyang Han , Jingjing Zhang , Ruoyu Wen , Barrett Ens , Adrian Clark , Tham Piumsomboon This is my paper · ORCID
classification cs.HC
keywords ExtendedrealityAI-drivenvisualizationDecisionmakingUserautonomyAItransparencyContext-awareinterfaces360-degreevideosimulationHuman-AIinteraction
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper asks whether the way an AI assistant visualizes guidance in extended reality (XR)—augmented or virtual environments—changes everyday decision-making, and whether one style fits all. The authors built four AI visualization techniques along an autonomy scale—Inform, Nudge, Recommend, and Instruct—and showed all four to eight participants in a simulated supermarket trip using a 360-degree video viewed through a VR headset, asking them to rank the techniques after each of six scripted events. The paper's central claim is that preference is context-dependent: familiar, low-stakes tasks favor simple information delivery, unfamiliar or money- and health-related choices favor ranked comparisons and suggestions, and urgent situations are less sensitive to technique choice. The authors conclude that maintaining user autonomy, making AI reasoning transparent, and adapting the visualization to context are the key design requirements for AI-driven visualization in XR.

What carries the argument

The central object is a four-level scale of AI autonomy adapted from existing level-of-automation taxonomies: Inform (AI supplies facts, the human decides), Nudge (AI offers gentle alternatives), Recommend (AI ranks a shortlist with justification), and Instruct (AI directs a single course of action). Each of six supermarket events was shown with all four techniques overlaid on a recorded 360-degree video, and participants ranked the techniques after every event, with a Latin-square order controlling sequence effects. This scale carries the argument because it lets differences in preference be attributed to how much autonomy the AI takes, rather than to arbitrary interface differences.

What would settle it

Run the same four techniques on real AR glasses in an actual supermarket with real time pressure and real money at stake and compare the event-by-event preference rankings with the video-based rankings; a different ordering—say, Instruct becoming preferred in real emergencies or Inform losing its lead in real price comparisons—would show the simulation is not carrying the intended decision pressure.

Watch

Extended reading notes

Core claim

On its own terms, the paper establishes that no single AI-driven visualization technique dominates across decision contexts. Inform ranked first when participants had to find products after an aisle rearrangement and when comparing prices across platforms; Recommend and Nudge led for out-of-stock and promotion events; and the fire-drill and interruption events produced close rankings with no clear winner. The authors interpret the pattern as evidence that AI visualization assistance must be context-aware, that users resist techniques that override their autonomy, and that users want to see the reasoning behind AI recommendations. They report these as design implications for future AR glasses interfaces rather than as a finalized quantitative result.

Load-bearing premise

The study assumes that watching a pre-recorded 360-degree video through a VR headset reproduces the pressures of wearing AR glasses in a real supermarket, so preferences measured in the simulation will carry over to real use.

Editorial extensions

If this is right

  • An AR-glasses assistant should not hard-code one visualization style; it should match the technique to the type of decision at hand.
  • For routine or familiar tasks, plain information delivery may outperform active AI suggestions, so a default Inform-like display is a reasonable starting point.
  • For choices involving money or health, ranked comparisons with visible reasoning are likely to be both preferred and trusted.
  • Interfaces that explain why the AI picked a recommendation will be necessary for trust, because several participants distrusted opaque ranking processes.
  • In disruption and interruption events the techniques appear interchangeable, so a simpler display may be sufficient there.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A natural extension the paper leaves implicit is an adaptive system that selects the visualization level from situational cues such as task novelty, stakes, and time pressure, then tests the selection rule in a field study.
  • The preference pattern can be read as a cognitive-effort trade-off: users accept more AI control when the decision is unfamiliar or high-stakes and reclaim control when the decision is cheap and familiar; measuring decision time and confidence per event would test this reading.
  • The near-tied rankings in the two disruption events could mean the techniques are redundant under stress, or that the simulation did not induce enough stress; a replication with heart-rate or skin-conductance measures could separate those possibilities.
  • Transparency could be isolated as a design axis by holding the technique fixed and varying only how much of the AI's reasoning is visible, which would test whether trust shifts as the interview data suggest.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 5 minor

Summary. The paper reports an exploratory study of four AI-driven visualisation techniques (Inform, Nudge, Recommend, Instruct) for decision support in extended reality, tested via a pre-recorded 360-degree supermarket video viewed in VR with an overlaid UI. An interdisciplinary workshop identified six stressful events, and eight participants ranked the techniques per event and took part in semi-structured interviews. The headline finding is that the preferred technique depends on event context, with design implications around user autonomy, AI transparency, and contextual adaptation.

Significance. If taken as a qualitative exploration, the study has value: it provides a structured design space for AI autonomy levels in XR visualisation, uses a Latin square design for ordering, and reports participant narratives that inform concrete design recommendations. The explicit classification of techniques and the fine-grained contextual events are useful starting points for future work. However, the paper's central empirical claim—that preference is context-dependent—rests on descriptive mean ranks from eight participants without any inferential statistics, so the quantitative basis is currently weak.

major comments (2)
  1. [§5.1, §6.1] The central claim that 'the preference for each technique is context-dependent' (§6.1) is supported only by mean preference ranks from eight participants (Figure 4, §5.1). No inferential statistics are reported for any comparison; the claim of 'no significant differences' for Events 5 and 6 appears without a test. Given n=8 and overlapping SDs (e.g., Event 6: Recommend mean 1.88, SD 0.64; Instruct mean 2.13, SD 1.25), the observed cross-event differences could easily arise from sampling error. Please either add a repeated-measures analysis (e.g., Friedman test on per-participant ranks) or revise the text to state explicitly that all findings are descriptive and exploratory.
  2. [§1, §4.1, §6.2] The study assumes that a pre-recorded 360° video viewed in VR adequately simulates AR glasses (§1, §4.1). This assumption is untested and directly affects the practical claims in §6.2, which are framed as recommendations for AR glasses. The paper should either provide evidence for the simulation's validity, or clearly limit the conclusions to the VR simulation and explicitly discuss this external-validity threat.
minor comments (5)
  1. [§5.1] The text contains several typos and formatting issues, including 'tehchnique' (Event 2), a missing space after the colon in 'Event 5 (Disruption):The', and inconsistent use of apostrophes around Nudge and Recommend; a careful copyedit is needed.
  2. [§2.1, References] Figure 2's caption contains 'autonomay' instead of 'autonomy', and reference [3] includes 'qualititative' instead of 'qualitative'.
  3. [§4.4] The Latin square design is mentioned but not described; please provide details on how the order of the four techniques was varied across the six events and the eight participants.
  4. [§5.1] For Event 3, the text lists Recommend before Nudge despite both having identical means (1.63); the ranking order should state how ties were handled.
  5. [Figure 4] Given the small sample size, Figure 4 would benefit from showing individual participant data or confidence intervals in addition to the means.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the central empirical claims rest on participant preference data and interviews, not on fitted equations or self-citation.

full rationale

This paper makes no formal derivation and contains no fitted parameters. The central claim that preference for AI-driven visualisation techniques is context-dependent (Section 6.1) is supported by descriptive preference rankings from eight participants (Section 5.1, Figure 4) and by qualitative interview feedback (Section 5.2). These data originate from the study participants, not from the authors' prior results or from any equations in the paper. The only self-citation that is plausibly connected to the technique design is reference [37], the authors' Ex-Cit XR work, which is cited for the terms 'Nudge' and 'Recommend' (Section 3.3.1). That use is inspirational and terminological, not load-bearing: the evaluation and the conclusions about user preferences do not reduce to that prior work. Similarly, reference [55] is a self-citation in the future-work discussion about asynchronous collaboration, and it does not support any empirical result in this paper. The statistical weakness identified by a skeptical reader, namely that the n=8 sample and lack of inferential tests make the context-dependence claim fragile, is a validity or robustness concern, not a circularity concern. The paper does not derive its findings from its own assumptions by definition, does not rename known results, and does not import uniqueness theorems from the authors' earlier work. Accordingly, no circular step can be quoted, and the appropriate score is 0.

Assumptions & free parameters 0 free parameters · 4 assumptions · 1 invented entities

The paper introduces no fitted numerical parameters; its 'result' is a set of preference rankings and qualitative themes. The main burdens are the simulation validity assumption, the applicability of autonomy frameworks, the realism of the scripted events, and the reliability of self-report. The four visualization techniques are author-invented design entities evaluated only in this exploratory study.

assumptions (4)
  • domain assumption The Parasuraman and O'Neill autonomy-level frameworks are an appropriate basis for designing and categorizing visualization techniques.
    The four-level design (Inform, Nudge, Recommend, Instruct) maps onto these frameworks in Section 3.3.1. If these frameworks do not apply to XR visualization, the technique set loses its theoretical grounding.
  • domain assumption A 360-degree video in VR simulates AR glasses for the purpose of this user study.
    Stated in Section 4.1: 'we simulate the functionalities of AR glasses by using 360-degree video in Virtual Reality.' The preference results are interpreted as informative for AR glasses design.
  • domain assumption The six workshop-derived events (aisle rearrangement, out-of-stock, sale, price comparison, fire drill, phone call) represent realistic stressors (time, finance, health) in daily shopping.
    Section 3.3.2. If the events do not actually induce time, financial, or health pressure, the context-dependence conclusions are weaker.
  • domain assumption Self-reported ranking and semi-structured interview responses reflect users' true decision-making preferences.
    Sections 4.4 and 5. The entire analysis rests on participants' verbal and ranked reports rather than on measured decisions or behavior.
invented entities (1)
  • The four AI-driven visualization techniques: Inform, Nudge, Recommend, Instruct.
    purpose: Operationalize four levels of AI autonomy in XR visualization to support user decision-making.
    These are novel design constructs introduced by the authors. The paper provides in-study preference data, but no external or longitudinal evidence; they are not established entities in prior literature.

how reviews work

0 comments
Cite this review

Pith. "Pith review of An Exploratory Study on AI-driven Visualisation Techniques on Decision Making in Extended Reality." pith.science (2026). https://pith.science/paper/CNEZWALC

@misc{pith2026250710981,
  author       = {Pith},
  title        = {Pith review of: An Exploratory Study on AI-driven Visualisation Techniques on Decision Making in Extended Reality},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CNEZWALC}},
  note         = {Machine review of arXiv:2507.10981}
}
read the original abstract

The integration of extended reality (XR) with artificial intelligence (AI) introduces a new paradigm for user interaction, enabling AI to perceive user intent, stimulate the senses, and influence decision-making. We explored the impact of four AI-driven visualisation techniques -- `Inform,' `Nudge,' `Recommend,' and `Instruct' -- on user decision-making in XR using the Meta Quest Pro. To test these techniques, we used a pre-recorded 360-degree video of a supermarket, overlaying each technique through a virtual interface. We aimed to investigate how these different visualisation techniques with different levels of user autonomy impact preferences and decision-making. An exploratory study with semi-structured interviews provided feedback and design recommendations. Our findings emphasise the importance of maintaining user autonomy, enhancing AI transparency to build trust, and considering context in visualisation design.

Figures

Figures reproduced from arXiv: 2507.10981 by the authors.

Figure 1
Figure 1. The exploratory study setup and screenshots of the experiences: a) experimental setup, b) introduction to Event 1 (E1), [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. The agent autonomay level adapted by Parasuraman et al. [33] and O’Neill et al. [31] [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. All four visualisation techniques, Inform, Nudge, Recommend, and Instruct, across six events. [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: The rankings in terms of user preferences for each visualisation technique for the six events. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

60 extracted references · 53 canonical work pages

  1. [1]

    Ashraf Abdul, Jo Vermeulen, Danding Wang, Brian Y Lim, and Mohan Kankan- halli. 2018. Trends and trajectories for explainable, accountable and intelligible systems: An hci research agenda. In Proceedings of the 2018 CHI conference on human factors in computing systems . ACM, Montreal, QC, Canada, 1–18

    work page 2018

  2. [2]

    Michael Abrash. 2021. Creating the future: Augmented reality, the next human-machine interface. In 2021 IEEE International Electron Devices Meeting (IEDM). IEEE, San Francisco, CA, USA, 1–2. https://ieeexplore.ieee.org/abstract/ document/9720526/

    work page arXiv 2021

  3. [3]

    Anne Adams, Peter Lunt, and Paul Cairns. 2008. A qualititative approach to HCI research. (2008), 138–157

    work page 2008

  4. [4]

    Alonso- Moral, Roberto Confalonieri, Riccardo Guidotti, Javier Del Ser, Natalia Díaz- Rodríguez, and Francisco Herrera

    Sajid Ali, Tamer Abuhmed, Shaker El-Sappagh, Khan Muhammad, Jose M. Alonso- Moral, Roberto Confalonieri, Riccardo Guidotti, Javier Del Ser, Natalia Díaz- Rodríguez, and Francisco Herrera. 2023. Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence. Information fusion 99 (2023), 101805. https...

    work page 2023

  5. [5]

    De Vreese

    Theo Araujo, Natali Helberger, Sanne Kruikemeier, and Claes H. De Vreese

    work page

  6. [6]

    Gagan Bansal, Tongshuang Wu, Joyce Zhou, Raymond Fok, Besmira Nushi, Ece Kamar, Marco Tulio Ribeiro, and Daniel Weld. 2021. Does the whole exceed its parts? the effect of ai explanations on complementary team performance. In Proceedings of the 2021 CHI conference on human factors in computing systems . Association for Computing Machinery, Yokohama, Japan, 1–16

    work page 2021

  7. [7]

    Ann Blandford, Dominic Furniss, and Stephann Makri. 2016. Qualitative HCI research: Going behind the scenes . Morgan & Claypool Publishers, San Rafael, CA, USA

    work page 2016

  8. [8]

    Runze Cai, Nuwan Janaka, Yang Chen, Lucia Wang, Shengdong Zhao, and Can Liu

    work page

Show all 60 references
  1. [9]

    Rafael A Calvo, Dorian Peters, Karina Vold, and Richard M Ryan. 2020. Supporting human autonomy in AI systems: A framework for ethical enquiry.Ethics of digital well-being: A multidisciplinary approach 140 (2020), 31–54

    2020

  2. [10]

    P Chandana Charitha and B Hemaraju. 2023. Impact of Artificial Intelligence on Decision-making in Organisations. Int. J. Multidiscip. Res 5 (2023)

    2023

  3. [11]

    Yuetpang Chen, Lin Yang, and Marisabel Chang. 2023. ShootPro: An Interactive and Immersive Basketball Shooting Practice Assistance System using Artificial Intelligence and Computer Vision. In CS & IT Conference Proceedings . CS & IT Conference Proceedings, IEEE, city, country

    2023

  4. [12]

    Edward De Bono. 2017. Six Thinking Hats: The multi-million bestselling guide to running better meetings and making faster decisions . Penguin uk, Unknown

    2017

  5. [13]

    Plecher, Tobias Mesmer, Lucas Leder, Tim Sime- cek, Nassim Boukadida, and Gudrun Klinker

    Christian Eichhorn, David A. Plecher, Tobias Mesmer, Lucas Leder, Tim Sime- cek, Nassim Boukadida, and Gudrun Klinker. 2023. Shopping in between Realities-Using an Augmented Virtuality Smartphone in a Virtual Supermarket. In 2023 IEEE International Symposium on Mixed and Augme...

    2023

  6. [14]

    Geoffrey Ellis and Alan Dix. 2007. A taxonomy of clutter reduction for information visualisation. IEEE transactions on visualization and computer graphics 13, 6 (2007), 1216–1223

    2007

  7. [15]

    João Marcelo Evangelista Belo, Mathias N Lystbæk, Anna Maria Feit, Ken Pfeuffer, Peter Kán, Antti Oulasvirta, and Kaj Grønbæk. 2022. Auit–the adaptive user interfaces toolkit for designing xr applications. In Proceedings of the 35th Annual ACM Symposium on User Interface Softw...

    2022

  8. [16]

    Nuša Farič, Sue Hinder, Robin Williams, Rishi Ramaesh, Miguel O Bernabeu, Edwin van Beek, and Kathrin Cresswell. 2024. Early experiences of integrating an artificial intelligence-based diagnostic decision support system into radiol- ogy settings: a qualitative study. Journal o...

    2024

  9. [17]

    Teresa Hirzle, Florian Müller, Fiona Draxler, Martin Schmitz, Pascal Knierim, and Kasper Hornbæk. 2023. When XR and AI Meet - A Scoping Review on Extended Reality and Artificial Intelligence. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems . ACM...

    2023

  10. [18]

    Nuwan Janaka, Runze Cai, Shengdong Zhao, and David Hsu. 2024. Demonstrat- ing PANDALens: Enhancing Daily Activity Documentation with AI-assisted In-Context Writing on OHMD. In Extended Abstracts of the 2024 CHI Con- ference on Human Factors in Computing Systems (CHI EA ’24) . ...

    2024

  11. [19]

    Daniel Keim, Gennady Andrienko, Jean-Daniel Fekete, Carsten Görg, Jörn Kohlhammer, and Guy Melançon. 2008. Visual analytics: Definition, process, and challenges. Springer, city, country

    2008

  12. [20]

    Somin Kim, Myeongul Jung, Jiwoong Heo, and Kwanguk Kenny Kim. 2023. In- teraction between AR Cue Types and Environmental Conditions in Autonomous Vehicles. In 2023 IEEE International Symposium on Mixed and Augmented Reality (ISMAR). IEEE, Sydney, NSW, Australia, 376–385. https...

    2023

  13. [21]

    Arto Laitinen and Otto Sahlgren. 2021. AI systems and respect for human autonomy. Frontiers in artificial intelligence 4 (2021), 705164

    2021

  14. [22]

    Richen Liu, Min Gao, Lijun Wang, Xiaohan Wang, Yuzhe Xiang, Aolin Zhang, Ji- azhi Xia, Yi Chen, and Siming Chen. 2022. Interactive extended reality techniques in information visualization. IEEE Transactions on Human-Machine Systems 52, 6 (2022), 1338–1351. https://ieeexplore.i...

    2022

  15. [23]

    Yuxin Liu and Keng L Siau. 2023. Human-AI Interaction and AI Avatars. In International Conference on Human-Computer Interaction. Springer, Springer, City, Country, 120–130

    2023

  16. [24]

    Feiyu Lu, Yan Xu, Xuhai Xu, Brennan Jones, and Laird Malamed. 2023. Exploring the Impact of User and System Factors on Human-AI Interactions in Head-Worn Displays. In 2023 IEEE International Symposium on Mixed and Augmented Reality (ISMAR). IEEE, Sydney, NSW, Australia, 109–11...

    2023

  17. [25]

    Sehi LYi, Jaemin Jo, and Jinwook Seo. 2020. Comparative layouts revisited: Design space, guidelines, and future directions. IEEE Transactions on Visualization and Computer Graphics 27, 2 (2020), 1525–1535

    2020

  18. [26]

    Shuai Ma, Chenyi Zhang, Xinru Wang, Xiaojuan Ma, and Ming Yin. 2024. Beyond Recommender: An Exploratory Study of the Effects of Different AI Roles in AI-Assisted Decision Making. arXiv preprint arXiv:2403.01791 (2024). https: //arxiv.org/abs/2403.01791

    2024 arXiv

  19. [27]

    Rasel Mahmud, Alberto Cordova, and John Quarles

    M. Rasel Mahmud, Alberto Cordova, and John Quarles. 2023. Visual cues for a steadier you: visual feedback methods improved standing balance in virtual reality for people with balance impairments. IEEE transactions on visualization and computer graphics (2023). https://ieeexplo...

    2023

  20. [28]

    Nuno Cid Martins, Bernardo Marques, João Alves, Tiago Araújo, Paulo Dias, and Beatriz Sousa Santos. 2022. Augmented reality situated visualization in decision-making. Multimedia Tools and Applications 81, 11 (2022), 14749–14772

    2022

  21. [29]

    Coleman Merenda, Missie Smith, Joseph Gabbard, Gary Burnett, and David Large

  22. [30]

    Mahdi H Miraz, Maaruf Ali, and Peter S Excell. 2021. Adaptive user interfaces and universal usability through plasticity of user interface design. Computer Science Review 40 (2021), 100363

    2021

  23. [31]

    Thomas O’Neill, Nathan McNeese, Amy Barron, and Beau Schelble. 2022. Hu- man–Autonomy Teaming: A Review and Analysis of the Empirical Literature. Human Factors: The Journal of the Human Factors and Ergonomics Society 64, 5 (Aug. 2022), 904–938. https://doi.org/10.1177/0018720820960865

    2022 doi

  24. [32]

    Orestis Papakyriakopoulos, Elizabeth Anne Watkins, Amy Winecoff, Klaudia Jaźwińska, and Tithi Chattopadhyay. 2021. Qualitative Analysis for Human Centered AI. arXiv preprint arXiv:2112.03784 (2021)

    2021 arXiv

  25. [33]

    Sheridan, and Christopher D

    Raja Parasuraman, Thomas B. Sheridan, and Christopher D. Wickens. 2000. A model for types and levels of human interaction with automation. IEEE Transac- tions on systems, man, and cybernetics-Part A: Systems and Humans 30, 3 (2000), 286–297. https://ieeexplore.ieee.org/abstrac...

    2000

  26. [34]

    Seonwook Park, Christoph Gebhardt, Roman Rädle, Anna Maria Feit, Hana Vrzakova, Niraj Ramesh Dayama, Hui-Shyong Yeo, Clemens N Klokmose, Aaron Quigley, Antti Oulasvirta, et al. 2018. Adam: Adapting multi-user interfaces for collaborative environments in real-time. In Proceedin...

    2018

  27. [35]

    Gloria Phillips-Wren and Monica Adya. 2020. Decision making under stress: the role of information overload, time pressure, complexity, and uncertainty. Journal of Decision Systems 29, sup1 (Aug. 2020), 213–225. https://doi.org/10. 1080/12460125.2020.1768680

    2020

  28. [36]

    Jiamin Ping, Bruce H Thomas, James Baumeister, Jie Guo, Dongdong Weng, and Yue Liu. 2020. Effects of shading model and opacity on depth perception in optical Exploratory AI-driven Visualisation Techniques in XR Conference acronym ’XX, June 03–05, 2018, Woodstock, NY see-throug...

    2020

  29. [37]

    Thammathip Piumsomboon, Gavin Ong, Cameron Urban, Barrett Ens, Jack Topliss, Xiaoliang Bai, and Simon Hoermann. 2022. Ex-Cit XR: Expert-elicitation and validation of Extended Reality visualisation and interaction techniques for dis- engaging and transitioning users from immers...

    2022

  30. [38]

    Abbasi, Muhammad Ali Imran, and Junaid Qadir

    Adnan Qayyum, Muhammad Atif Butt, Hassan Ali, Muhammad Usman, Osama Halabi, Ala Al-Fuqaha, Qammer H. Abbasi, Muhammad Ali Imran, and Junaid Qadir. 2024. Secure and Trustworthy Artificial Intelligence-extended Reality (AI-XR) for Metaverses. Comput. Surveys 56, 7 (July 2024), 1...

    2024 doi

  31. [39]

    Dirk Reiners, Mohammad Reza Davahli, Waldemar Karwowski, and Carolina Cruz-Neira. 2021. The combination of artificial intelligence and extended reality: A systematic review. Frontiers in Virtual Reality 2 (2021), 721933. https://www. frontiersin.org/articles/10.3389/frvir.2021...

    2021

  32. [40]

    Sara Romano, Enricoandrea Laviola, Michele Gattullo, Michele Fiorentino, and Antonio Emmanuele Uva. 2023. More Arrows in the Quiver: investigating the use of auxiliary models to localize in-view components with augmented reality. IEEE Transactions on Visualization and Computer...

    2023

  33. [41]

    Quentin Roy, Futian Zhang, and Daniel Vogel. 2019. Automation accuracy is good, but high controllability may be better. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems . Association for Computing Machinery, Glasgow, Scotland, UK, 1–8

    2019

  34. [42]

    Sadeghi, Alexander PWM Maat, Yannick JHJ Taverne, Robin Cornelissen, Anne-Marie C

    Amir H. Sadeghi, Alexander PWM Maat, Yannick JHJ Taverne, Robin Cornelissen, Anne-Marie C. Dingemans, Ad JJC Bogers, and Edris AF Mahtab. 2021. Virtual reality and artificial intelligence for 3-dimensional planning of lung segmentec- tomies. JTCVS techniques 7 (2021), 309–321....

    2021

  35. [43]

    Dominik Siemon. 2022. Elaborating Team Roles for Artificial Intelligence-based Teammates in Human-AI Collaboration. Group Decision and Negotiation 31, 5 (October 2022), 871–912. https://doi.org/10.1007/s10726-022-09792-

    2022 doi

  36. [44]

    Satyendra Singh. 2006. Impact of color on marketing. Management decision 44, 6 (2006), 783–789

    2006

  37. [45]

    Kashyap Todi, Gilles Bailly, Luis Leiva, and Antti Oulasvirta. 2021. Adapting user interfaces with model-based reinforcement learning. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems . Association for Computing Machinery, Yokohama, Japan, 1–13

    2021

  38. [46]

    Wan-Lun Tsai, Li-Wen Su, Tsai-Yen Ko, Tse-Yu Pan, and Min-Chun Hu. 2021. Feasibility study on using AI and VR for decision-making training of basketball players. IEEE Transactions on Learning Technologies 14, 6 (2021), 754–762

    2021

  39. [47]

    Viture. 2024. VITURE: Next Gen XR Glasses. https://www.viture.com/

    2024

  40. [48]

    Carolin Wienrich and Marc Erich Latoschik. 2021. extended artificial intelligence: New prospects of human-ai interaction research. Frontiers in Virtual Reality 2 (2021), 686783

    2021

  41. [49]

    Aoyu Wu, Yun Wang, Xinhuan Shu, Dominik Moritz, Weiwei Cui, Haidong Zhang, Dongmei Zhang, and Huamin Qu. 2021. Ai4vis: Survey on artificial intelligence approaches for data visualization. IEEE Transactions on Visualization and Computer Graphics 28, 12 (2021), 5049–5070

    2021

  42. [50]

    Hsiao-ping Wu, Esther Garza, and Norma Guzman. 2015. International Student’s Challenge and Adjustment to College. Education Research International 2015 (2015), 1–9. https://doi.org/10.1155/2015/202753

    2015 doi

  43. [51]

    Xreal. 2024. XREAL - Building Augmented Reality for Everyone. https://www. xreal.com/

    2024

  44. [52]

    Xuhai Xu, Anna Yu, Tanya R. Jonker, Kashyap Todi, Feiyu Lu, Xun Qian, João Marcelo Evangelista Belo, Tianyi Wang, Michelle Li, Aran Mun, Te-Yen Wu, Junxiao Shen, Ting Zhang, Narine Kokhlikyan, Fulton Wang, Paul Soren- son, Sophie Kim, and Hrvoje Benko. 2023. XAIR: A Framework ...

    2023

  45. [53]

    Rongbin Yang and Santoso Wibowo. 2022. User trust in artificial intelligence: A comprehensive conceptual framework.Electronic Markets 32, 4 (2022), 2053–2077

    2022

  46. [54]

    Hyoseok Yoon. 2021. Opportunities and challenges of smartglass-assisted in- teractive telementoring. Applied System Innovation 4, 3 (2021), 56. https: //www.mdpi.com/2571-5577/4/3/56 Publisher: MDPI

    2021

  47. [55]

    Jingjing Zhang, Binyang Han, Ze Dong, Ruoyu Wen, Gun A Lee, Simon Hoer- mann, Wendy Zhang, and Thammathip Piumsomboon. 2024. Virtual Triplets: A Mixed Modal Synchronous and Asynchronous Collaboration with Human-Agent Interaction in Virtual Reality. In Extended Abstracts of the...

    2024

  48. [56]

    Hui Zhu, Eva-Lotta Sallnäs Pysander, and Inga-Lill Söderberg. 2023. Not trans- parent and incomprehensible: A qualitative user study of an AI-empowered financial advisory system. Data and Information Management 7, 3 (2023), 100041. https://doi.org/10.1016/j.dim.2023.100041 Spe...

    2023

  49. [57]

    Roberto V Zicari, John Brodersen, James Brusseau, Boris Düdder, Timo Eichhorn, Todor Ivanov, Georgios Kararigas, Pedro Kringen, Melissa McCullough, Florian Möslein, et al. 2021. Z-Inspection ®: a process to assess trustworthy AI. IEEE Transactions on Technology and Society 2, ...

    2021

  50. [2016]

    In 2016 IEEE VR 2016 Workshop on Perceptual and Cognitive Issues in AR (PERCAR)

    Effects of real-world backgrounds on user interface color naming and matching in automotive AR HUDs. In 2016 IEEE VR 2016 Workshop on Perceptual and Cognitive Issues in AR (PERCAR) . IEEE, IEEE, Greenville, SC, USA, 1–6

    2016

  51. [2020]

    AI & SOCIETY 35, 3 (Sept

    In AI we trust? Perceptions about automated decision-making by artificial intelligence. AI & SOCIETY 35, 3 (Sept. 2020), 611–623. https://doi.org/10.1007/ s00146-019-00931-w

    2020

  52. [2024]

    In Proceedings of the CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’24)

    PANDALens: Towards AI-Assisted In-Context Writing on OHMD During Travels. In Proceedings of the CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’24). Association for Computing Machinery, New York, NY, USA, Article 1053, 24 pages. https://doi.org/1...

    doi

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.